# Install required packages import gradio as gr import json import cv2 import numpy as np from deepface import DeepFace import matplotlib.pyplot as plt from PIL import Image import tempfile import os import pandas as pd import shutil def verify_faces(img1, img2, threshold=0.70, model="VGG-Face"): temp_dir = tempfile.mkdtemp() img1_path = os.path.join(temp_dir, "image1.jpg") img2_path = os.path.join(temp_dir, "image2.jpg") if isinstance(img1, np.ndarray): Image.fromarray(img1).save(img1_path) else: img1.save(img1_path) if isinstance(img2, np.ndarray): Image.fromarray(img2).save(img2_path) else: img2.save(img2_path) try: result = DeepFace.verify( img1_path=img1_path, img2_path=img2_path, model_name=model, distance_metric="cosine", threshold=threshold ) fig, ax = plt.subplots(1, 2, figsize=(10, 5)) img1_display = cv2.imread(img1_path) img1_display = cv2.cvtColor(img1_display, cv2.COLOR_BGR2RGB) img2_display = cv2.imread(img2_path) img2_display = cv2.cvtColor(img2_display, cv2.COLOR_BGR2RGB) ax[0].imshow(img1_display) ax[0].set_title("Image 1") ax[0].axis("off") ax[1].imshow(img2_display) ax[1].set_title("Image 2") ax[1].axis("off") verification_result = "✅ FACE MATCHED" if result["verified"] else "❌ FACE NOT MATCHED" confidence = round((1 - result["distance"]) * 100, 2) plt.suptitle(f"{verification_result}\nConfidence: {confidence}%\nDistance: {result['distance']:.4f}", fontsize=16, fontweight='bold', color='green' if result["verified"] else 'red') plt.tight_layout() os.remove(img1_path) os.remove(img2_path) os.rmdir(temp_dir) return fig, json.dumps(result, indent=2) except Exception as e: if os.path.exists(img1_path): os.remove(img1_path) if os.path.exists(img2_path): os.remove(img2_path) if os.path.exists(temp_dir): os.rmdir(temp_dir) error_msg = f"Error: {str(e)}" if "No face detected" in str(e): error_msg = "No face detected in one or both images. Please try different images." return None, error_msg def find_faces(query_img, db_folder, threshold=0.70, model="VGG-Face"): temp_dir = tempfile.mkdtemp() query_path = os.path.join(temp_dir, "query.jpg") if isinstance(query_img, np.ndarray): Image.fromarray(query_img).save(query_path) else: query_img.save(query_path) if isinstance(db_folder, str): db_path = db_folder else: db_path = os.path.join(temp_dir, "db") os.makedirs(db_path, exist_ok=True) for i, file in enumerate(db_folder): orig_filename = file.orig_name # Get original filename with extension file_ext = os.path.splitext(orig_filename)[1] new_filename = f"image_{i}{file_ext}" shutil.copy(file.name, os.path.join(db_path, new_filename)) try: dfs = DeepFace.find( img_path=query_path, db_path=db_path, model_name=model, distance_metric="cosine", threshold=threshold, silent=True # Disable unnecessary logging ) if isinstance(dfs, list): if len(dfs) == 0: return None, "No matching faces found in the database." df = dfs[0] else: df = dfs if df.empty: return None, "No matching faces found in the database." df = df.sort_values(by=["distance"]) num_matches = min(4, len(df)) fig, axes = plt.subplots(1, num_matches + 1, figsize=(15, 5)) query_display = cv2.imread(query_path) query_display = cv2.cvtColor(query_display, cv2.COLOR_BGR2RGB) axes[0].imshow(query_display) axes[0].set_title("Query Image") axes[0].axis("off") for i in range(num_matches): match_path = df.iloc[i]["identity"] if not os.path.exists(match_path): continue # Skip invalid paths distance = df.iloc[i]["distance"] confidence = round((1 - distance) * 100, 2) match_img = cv2.imread(match_path) if match_img is None: continue # Skip unreadable images match_img = cv2.cvtColor(match_img, cv2.COLOR_BGR2RGB) axes[i+1].imshow(match_img) axes[i+1].set_title(f"Match #{i+1}\nConfidence: {confidence}%") axes[i+1].axis("off") plt.suptitle(f"Found {len(df)} matching faces", fontsize=16, fontweight='bold') plt.tight_layout() results = df[["identity", "distance"]].copy() results["confidence"] = (1 - results["distance"]) * 100 results["confidence"] = results["confidence"].round(2) results = results.rename(columns={"identity": "Image Path"}) os.remove(query_path) if not isinstance(db_folder, str): shutil.rmtree(db_path) return fig, results.to_dict('records') except Exception as e: if os.path.exists(query_path): os.remove(query_path) error_msg = f"Error: {str(e)}" if "No face detected" in str(e): error_msg = "No face detected in the query image. Please try a different image." elif "No such file or directory" in str(e): error_msg = "Invalid database path or corrupted image files" return None, error_msg def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']): temp_dir = tempfile.mkdtemp() img_path = os.path.join(temp_dir, "analyze.jpg") if isinstance(img, np.ndarray): Image.fromarray(img).save(img_path) else: img.save(img_path) try: results = DeepFace.analyze( img_path=img_path, actions=actions, enforce_detection=True, detector_backend='opencv' ) if isinstance(results, list): num_faces = len(results) else: num_faces = 1 results = [results] fig = plt.figure(figsize=(14, 7)) img_display = cv2.imread(img_path) img_display = cv2.cvtColor(img_display, cv2.COLOR_BGR2RGB) main_ax = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2) main_ax.imshow(img_display) main_ax.set_title(f"Analyzed Image ({num_faces} face{'s' if num_faces > 1 else ''} detected)") main_ax.axis('off') for i, face_result in enumerate(results): if i >= 4: break age = face_result.get('age', 'N/A') gender = face_result.get('dominant_gender', 'N/A') race = face_result.get('dominant_race', 'N/A') emotion = face_result.get('dominant_emotion', 'N/A') gender_conf = 'N/A' if 'gender' in face_result and isinstance(face_result['gender'], dict): for g, conf in face_result['gender'].items(): if g.lower() == gender.lower(): gender_conf = f"{conf:.1f}%" break race_conf = 'N/A' if 'race' in face_result and isinstance(face_result['race'], dict): for r, conf in face_result['race'].items(): if r.lower() == race.lower(): race_conf = f"{conf:.1f}%" break emotion_conf = 'N/A' if 'emotion' in face_result and isinstance(face_result['emotion'], dict): for e, conf in face_result['emotion'].items(): if e.lower() == emotion.lower(): emotion_conf = f"{conf:.1f}%" break ax = plt.subplot2grid((2, 4), (0 if i < 2 else 1, 2 + (i % 2))) text = ( f"Face #{i+1}\n\n" f"Age: {age}\n\n" f"Gender: {gender} ({gender_conf})\n\n" f"Race: {race} ({race_conf})\n\n" f"Emotion: {emotion} ({emotion_conf})" ) ax.text(0.5, 0.5, text, ha='center', va='center', fontsize=11) ax.axis('off') plt.tight_layout() os.remove(img_path) os.rmdir(temp_dir) formatted_results = [] for i, res in enumerate(results[:8]): face_data = { "face_number": i+1, "age": res.get("age", "N/A"), "gender": { "dominant": res.get("dominant_gender", "N/A"), "confidence": res.get("gender", {}) }, "race": { "dominant": res.get("dominant_race", "N/A"), "confidence": res.get("race", {}) }, "emotion": { "dominant": res.get("dominant_emotion", "N/A"), "confidence": res.get("emotion", {}) } } formatted_results.append(face_data) return fig, formatted_results except Exception as e: if os.path.exists(img_path): os.remove(img_path) if os.path.exists(temp_dir): os.rmdir(temp_dir) error_msg = f"Error: {str(e)}" if "No face detected" in str(e): error_msg = "No face detected in the image. Please try a different image." return None, error_msg # Create Gradio interface with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 🔍 Complete Face Recognition Tool This tool provides three face recognition features: - **Verify Faces**: Compare two specific images to check if they contain the same person - **Find Faces**: Search for matching faces in a database/folder - **Analyze Face**: Determine age, gender, race, and emotion from a facial image """) with gr.Tabs(): with gr.TabItem("Verify Faces"): with gr.Row(): img1_input = gr.Image(label="First Image", type="pil") img2_input = gr.Image(label="Second Image", type="pil") with gr.Row(): verify_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05, label="Similarity Threshold (lower = stricter matching)") verify_model = gr.Dropdown( choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"], value="VGG-Face", label="Face Recognition Model" ) verify_button = gr.Button("Verify Faces", variant="primary") verify_result_plot = gr.Plot(label="Verification Result") verify_json = gr.JSON(label="Technical Details") verify_button.click( verify_faces, inputs=[img1_input, img2_input, verify_threshold, verify_model], outputs=[verify_result_plot, verify_json] ) gr.Markdown("""... (keep existing markdown) ...""") with gr.TabItem("Find Faces"): query_img = gr.Image(label="Query Image (Face to find)", type="pil") db_path_input = gr.Textbox(label="Database Path (folder containing images to search in)") db_files_input = gr.File(label="Or upload images for database", file_count="multiple") with gr.Row(): find_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05, label="Similarity Threshold (lower = stricter matching)") find_model = gr.Dropdown( choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"], value="VGG-Face", label="Face Recognition Model" ) find_button = gr.Button("Find Matching Faces", variant="primary") find_result_plot = gr.Plot(label="Search Results") find_results_table = gr.JSON(label="Detailed Results") find_button.click( find_faces, inputs=[query_img, db_path_input, find_threshold, find_model], outputs=[find_result_plot, find_results_table] ) db_files_input.change( lambda x: "", inputs=db_files_input, outputs=db_path_input ) gr.Markdown("""... (keep existing markdown) ...""") with gr.TabItem("Analyze Face"): analyze_img = gr.Image(label="Upload Image for Analysis", type="pil") actions_checkboxes = gr.CheckboxGroup( choices=["age", "gender", "race", "emotion"], value=["age", "gender", "race", "emotion"], label="Select Attributes to Analyze" ) analyze_button = gr.Button("Analyze Face", variant="primary") analyze_result_plot = gr.Plot(label="Analysis Results") analyze_json = gr.JSON(label="Detailed Analysis") analyze_button.click( analyze_face, inputs=[analyze_img, actions_checkboxes], outputs=[analyze_result_plot, analyze_json] ) gr.Markdown("""... (keep existing markdown) ...""") # Launch the app demo.launch()